基于区域检测与非下采样轮廓波变换的红外与彩色可见光图像融合

针对灰度图像融合的分辨率低及现有的彩色图像融合方法融合的图像色彩不自然、不符合人的视觉感受的特点,在此提出一种基于Snake模型的区域检测和非下采样轮廓波变换(NSCT)的红外与彩色可见光图像融合的方法。首先对彩色可见光图像进行亮度、色度和饱和度(IHS)颜色空间变换提取亮度分量,并用Snake模型对红外图像的目标区域进行检测;然后对亮度分量和目标替换的红外图像应用NSCT分解,对所得到的高频系数采用像素点"绝对值和取大"、低频系数采用基于"亮度重映射技术"的加权融合规则进行融合;通过对融合系数进行NSCT逆变换获得融合图像的亮度分量,最后运用颜色空间逆变换得到融合图像。实验结果表明,所提出的融合方法既能保持可见光图像的高分辨率和自然色彩,又能准确保留红外图像中检测出的目标信息,获得视觉效果较好、综合指标较优的融合图像。     

A multi-focus image fusion algorithm based on an improved dual-channel PCNN in NSCT domain

This paper presents a multi-focus image fusion algorithm based on dual-channel PCNN in NSCT domain. The fusion algorithm based on multi-scale transform is likely to produce the pseudo-Gibbs effects and it is not effective to fuse the dim or partial bright images. To solve these problems, this algorithm will get a number of different frequency sub-image of the two images by using the NSCT transform, the selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail, and the images are fused based on the improved dual-channel PCNN in order to determine the band-pass sub-band coefficient, at last fused image is obtained by using the inverse NSCT transform. Fusion rules based on dual-channel PCNN are used to solve the complexity of the PCNN parameter settings and long computing time problems. The experimental results show that the algorithm has overcome the defects of the traditional multi-focus image fusion algorithm and improved the fusion effect.     

非下采样变换的红外与可见光图像融合

基于非下采样Contourlet变换（NSCT）,提出了一种红外和可见光图像融合算法。针对低频子带系数和各带通方向子带系数分别提出了基于图像物理特征的系数加权选择方式与基于区域能量匹配的系数选择方式,即低频基于区域梯度信息、高频基于区域特征因子的加权与选择结合的图像融合算法。实验结果表明：非下采样Contourlet变换具有较快的运算速度,且经非下采样变换后能量更加集中,可提供更多的图像信息。相对于基于像素的图像融合算法,本文的图像融合算法具有更高的融合性能,是一种更适合图像融合的多尺度几何分析（MGA）工具。     

基于非采样Contourlet变换的遥感图像融合算法

为了使融合后的多光谱图像在尽可能保持原始多光谱图像光谱特性的同时,显著提高空间分辨力,提出了一种基于非采样Contourlet变换(NSCT)的遥感图像融合算法。算法首先对全色波段图像进行非采样Contourlet变换,得到全色波段图像的低频子带系数和各带通方向子带系数;然后针对多光谱图像的每一个波段,将其进行双线性插值后作为融合后多光谱图像的低频子带系数,对全色波段图像的各带通方向子带系数采用基于成像系统物理特性的注入模型(调整系数)进行局部调整后,作为融合后多光谱图像的各带通方向子带系数,从而得到融合后多光谱图像的非采样Contourlet变换系数;最后再经非采样Contourlet逆变换得到该波段具有高空间分辨力的多光谱图像。采用IKONOS卫星遥感图像进行了仿真实验,实验结果表明,该算法在光谱保留和空间质量提高方面优于其它传统的遥感图像融合算法。     

Pulse Coupled Neural Network-Based Multimodal Medical Image Fusion via Guided Filtering and WSEML in NSCT Domain

Multimodal medical image fusion aims to fuse images with complementary multisource information. In this paper, we propose a novel multimodal medical image fusion method using pulse coupled neural network (PCNN) and a weighted sum of eight-neighborhood-based modified Laplacian (WSEML) integrating guided image filtering (GIF) in non-subsampled contourlet transform (NSCT) domain. Firstly, the source images are decomposed by NSCT, several low- and high-frequency sub-bands are generated. Secondly, the PCNN-based fusion rule is used to process the low-frequency components, and the GIF-WSEML fusion model is used to process the high-frequency components. Finally, the fused image is obtained by integrating the fused low- and high-frequency sub-bands. The experimental results demonstrate that the proposed method can achieve better performance in terms of multimodal medical image fusion. The proposed algorithm also has obvious advantages in objective evaluation indexes VIFF, Q_W, API, SD, EN and time consumption.     

Multifocus image fusion based on features contrast of multiscale products in nonsubsampled contourlet transform domain

In this paper, an efficient multifocus image fusion approach is proposed based on local features contrast of multiscale products in nonsubsampled contourlet transform (NSCT) domain. In order to improve the robustness of the fusion algorithm to the noise and select the coefficients of the fused image properly, the multiscale products, which can distinguish edge structures from noise more effectively in NSCT domain, is developed and introduced into image fusion field. The selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail. To improve the quality of the fused image, novel different local features contrast measurements, which are proved to be more suitable for human vision system and can extract more useful detail information from source images and inject them into the fused image, are developed and used to select coefficients from the clear parts of subimages to compose coefficients of fused images. Experimental results demonstrate the proposed method performs very well in fusion both noisy and noise-free multifocus images, and outperform conventional methods in terms of both visual quality and objective evaluation criteria.     

An adaptive fusion approach for infrared and visible images based on NSCT and compressed sensing

A novel nonsubsampled contourlet transform (NSCT) based image fusion approach, implementing an adaptive-Gaussian (AG) fuzzy membership method, compressed sensing (CS) technique, total variation (TV) based gradient descent reconstruction algorithm, is proposed for the fusion computation of infrared and visible images.Compared with wavelet, contourlet, or any other multi-resolution analysis method, NSCT has many evident advantages, such as multi-scale, multi-direction, and translation invariance. As is known, a fuzzy set is characterized by its membership function (MF), while the commonly known Gaussian fuzzy membership degree can be introduced to establish an adaptive control of the fusion processing. The compressed sensing technique can sparsely sample the image information in a certain sampling rate, and the sparse signal can be recovered by solving a convex problem employing gradient descent based iterative algorithm(s).In the proposed fusion process, the pre-enhanced infrared image and the visible image are decomposed into low-frequency subbands and high-frequency subbands, respectively, via the NSCT method as a first step. The low-frequency coefficients are fused using the adaptive regional average energy rule; the highest-frequency coefficients are fused using the maximum absolute selection rule; the other high-frequency coefficients are sparsely sampled, fused using the adaptive-Gaussian regional standard deviation rule, and then recovered by employing the total variation based gradient descent recovery algorithm.Experimental results and human visual perception illustrate the effectiveness and advantages of the proposed fusion approach. The efficiency and robustness are also analyzed and discussed through different evaluation methods, such as the standard deviation, Shannon entropy, root-mean-square error, mutual information and edge-based similarity index.     

改进的非下采样Contourlet变形红外弱小目标检测方法

针对存在复杂背景干扰和噪声的红外图像弱小目标检测问题,提出了一种非下采样Contourlet变换（NSCT）的改进的红外弱小目标检测方法。首先对含弱小目标的红外图像进行预处理,然后利用NSCT进行变换,并利用改进的非线性映射函数和能量交叉融合相结合的方法实现了背景杂波的抑制,最后引入Otsu算法进行阈值分割分离出红外弱小目标。通过与同类弱小目标检测算法的对比实验,验证了该方法的有效性。     

一种基于NSCT变换的红外与可见光图像融合算法

针对红外与可见光图像融合,提出了一种基于NSCT变换的图像融合方法。对经NSCT变换的低频子带系数采用基于区域能量自适应加权的融合规则,对高频子带系数采用混合的融合方法,即对于低层,采用基于区域方差选大的融合方法,对于高层采用像素点的绝对值选大的融合方法。实验结果表明,该融合算法可以获得更多的细节信息,能获得较理想的融合图像。     

Image fusion algorithm based on redundant-lifting NSWMDA and adaptive PCNN

Pyramid decomposition in the NSCT transformation is a band-pass filtering process in the frequency domain where different scales of images are orthogonal. However, from the perspective of the image content, correlation is likely to exist between the fused images, and this kind of decomposition makes images of different scales contain redundant information, as a result of which the fused image may not capture the subtle information from the original images. In order to overcome the above-mentioned problem, an effective image fusion method based on redundant-lifting non-separable wavelet multi-directional analysis (NSWMDA) and adaptive pulse coupled neural network (PCNN) has been proposed. The original images are firstly decomposed by using the NSWMDA into several sub-bands in order to retain texture detail and contrast information of the images, and then adaptive PCNN algorithm is applied on the high-frequency directional sub-bands to extract the high-frequency information. The low-frequency sub-bands are evaluated by weighted average based on Gaussian kernel with a chosen maximum fusion rule. Results from experiments show that the proposed method can make the fused image maintains more texture details and contrast information.     

一种基于区域生长的红外与可见光的图像融合方法

针对目前红外与可见光目标跟踪的视频序列融合难以满足实时性问题，利用红外图像目标与背景显著的灰度差异特征，结合目标跟踪中目标分割时常用到的区域生长法，通过区域生长方法从红外图像中提取目标区域，再将得到的红外目标区域与已经过图像配准的可见光图像的背景区域进行融合处理，最终得到既具有红外图像较好的目标指示特性又具有可见光图像清晰场景信息的融合图像。实验表明：该算法不仅简单易行，而且所得到的融合图像视觉效果优于其他融合算法得到的图像。     

Fusion method of infrared and visible images based on neighborhood characteristic and regionalization in NSCT domain

On fusing infrared and visible image, the traditional fusion method cannot get the better image quality. Based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain, the fusion algorithm was proposed. Firstly, NSCT was adopted to decompose infrared and visible images at different scales and directions for the low and high frequency coefficients, the low frequency coefficients which were fused with improving regional weighted fusion method based on neighborhood energy, and the high-frequency coefficients were fused with multi-judgment rule based on neighborhood characteristic regional process. Finally, the coefficients were reconstructed to obtain the fused image. The experimental results show that, compared with the other three related methods, the proposed method can get the biggest value of IE (information entropy), MI(VI,F) (mutual information from visible image), MI(VI,F) (mutual information from infrared image), MI (sum of mutual information), and Q^AB/F (edge retention). The proposed method can leave enough information in the original images and its details, and the fused images have better visual effects.     

Infrared and visible image fusion scheme based on NSCT and low-level visual features

Multi-scale transform (MST) is an efficient tool for image fusion. Recently, many fusion methods have been developed based on different MSTs, and they have shown potential application in many fields. In this paper, we propose an effective infrared and visible image fusion scheme in nonsubsampled contourlet transform (NSCT) domain, in which the NSCT is firstly employed to decompose each of the source images into a series of high frequency subbands and one low frequency subband. To improve the fusion performance we designed two new activity measures for fusion of the lowpass subbands and the highpass subbands. These measures are developed based on the fact that the human visual system (HVS) percept the image quality mainly according to its some low-level features. Then, the selection principles of different subbands are presented based on the corresponding activity measures. Finally, the merged subbands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSCT on these merged subbands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.     

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.     

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.     

Multifocus image fusion and denoising scheme based on homogeneity similarity

A novel image fusion algorithm based on homogeneity similarity is proposed in this paper, aiming at solving the fusion problem of clean and noisy multifocus images. Firstly, the initial fused image is acquired with one multiresolution image fusion method. The pixels of the source images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions are determined. In order to improve the fusion performance, morphological opening and closing are employed for post-processing. Secondly, the homogeneity similarity is introduced and used to fuse the clean and noisy multifocus images. Finally, the fused image is obtained by weighting the neighborhood pixels of the point of source images which are located at the focused region. Experimental results demonstrate that, for the clean multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities. Furthermore, it can simultaneously resolve the image restoration and fusion problem when the source multifocus images are corrupted by the Gaussian white noise, and can also provide better performance than the conventional methods.     

The infrared and visible image fusion algorithm based on target separation and sparse representation

Although the fused image of the infrared and visible image takes advantage of their complementary, the artifact of infrared targets and vague edges seriously interfere the fusion effect. To solve these problems, a fusion method based on infrared target extraction and sparse representation is proposed. Firstly, the infrared target is detected and separated from the background rely on the regional statistical properties. Secondly, DENCLUE (the kernel density estimation clustering method) is used to classify the source images into the target region and the background region, and the infrared target region is accurately located in the infrared image. Then the background regions of the source images are trained by Kernel Singular Value Decomposition (KSVD) dictionary to get their sparse representation, the details information is retained and the background noise is suppressed. Finally, fusion rules are built to select the fusion coefficients of two regions and coefficients are reconstructed to get the fused image. The fused image based on the proposed method not only contains a clear outline of the infrared target, but also has rich detail information.     

基于目标识别的红外与微光图像融合方法

 为了在融合图像中突出运动目标,提出了一种基于动态目标检测和识别的图像融合算法。先对红外图像序列中的运动目标进行检测和提取,同时对红外和微光图像进行融合,最后将提取到的红外目标与融合图像进行二次融合。试验结果表明,该算法获得的融合图像不仅具有普通融合算法信息丰富的特点,还具有鲜明的红外目标指示特性。     

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.     

Corner detection and matching for infrared image based on double ring mask and adaptive SUSAN algorithm

Infrared (IR) image fusion is designed to fuse several IR images into a comprehensive image to boost imaging quality and reduce redundancy information, and image matching is an indispensable step. However, Conventional matching techniques are susceptible to the noise and fuzzy edges in IR images and it is therefore very desirable to have a matching algorithm that is tolerant to them. This paper presents a method for infrared image matching based on the SUSAN corner detection. To solve the problems of the traditional SUSAN algorithm including the fixed threshold of gray value difference and the failed detection of symmetry corners, an adaptive threshold extraction method is raised in this study. Furthermore, an attached double ring mask is used to improve the complex corner detection capability. A constraint condition and a principle of gravity are adopted to filtrate the candidate corners. The proposed method is qualitatively and quantitatively evaluated on IR images in the experiments. In comparison with other methods, better performance has been achieved.